Why India’s blanket licence matters to me
I have been watching the debate around how generative AI systems are trained with a mix of hope and worry. The recent push by the Centre — proposing a mandatory blanket licence that lets AI developers use all lawfully accessed copyrighted works for training while creating a central royalty-collection mechanism — is one of those policy moments that feels designed to force a national choice about innovation, fairness and cultural stewardship (Economic Times, TechCrunch).
I wrote about the urgency of shaping AI’s rules before — the risks, the runaway possibilities and the need for a public framework — in an earlier piece where I argued that warnings are useful only if we design solutions in time "Warning is good : Solutions are better". That same impatience is what makes this blanket-licence proposal so compelling and so alarming at once.
What the proposal promises (in plain terms)
- A single, mandatory licence that allows AI firms to use any content they lawfully access to train models.
- Statutory remuneration for creators administered by a central collecting body (often discussed as CRCAT or similar).
- Royalty rates set by a government-appointed committee and collected post-commercialisation, often as a percentage of revenue.
- Simplified compliance for developers and a single-window mechanism for rights-holders to be paid (PolicyEdge summary).
The intent is clear: remove the high transaction costs and legal uncertainty that make universal licensing impractical, while ensuring creators are compensated.
The upside I can see
- Practicality for startups: No one wants a thousand individual negotiations before training a model. A statutory, post-commercialisation payment reduces upfront barriers and encourages innovation.
- Cultural completeness: Models trained on richer, more diverse Indian-language and cultural content can be more useful for our country — an argument the proposal explicitly makes when it stresses India-centric benchmarks and multilingual evaluation.
- Redistribution potential: A well-run collecting body could reach otherwise unorganised creators and artists who today are invisible to the platform economy.
And the deep problems we must not ignore
- Loss of agency for creators: For many artists and authors, the ability to opt out or negotiate terms is part of how they control their work and livelihood. Mandating compulsory use without a granular consent mechanism risks disempowering creators.
- Distribution fairness: Central collection bodies often face governance capture. Without transparent, auditable distribution rules and strong representation for small creators, royalties can be swallowed by intermediaries.
- Technical opacity: The industry’s argument that you cannot trace which exact records “contributed” to a probabilistic model’s output is valid. That makes attribution-based micro-payments practically impossible — so revenue-sharing must be designed carefully to avoid perverse incentives.
- Global mismatch: If other jurisdictions adopt different rules, companies operating across borders will face complexity. India’s model would need clear rules about extraterritorial content and cross-border revenue.
Practical design principles I'd insist on
- Transparency by design
- Mandatory, machine-readable disclosures about categories and broad sources of training data (not proprietary details), audited by an independent regulator.
- Strong governance for the collecting body
- Democratic representation from many classes of creators, independent auditors, and open publishing of allocation rules and payments.
- Tiered royalty rules
- Different treatment for non-commercial research, educational uses and small-scale startups (for example, revenue thresholds or graduated rates) so innovation isn’t strangled at birth.
- Registry and outreach for unorganised creators
- A simple registry (with low friction) so musicians, writers and independent artists can claim their share; funds should be held for three years for works that register later, as some proposals suggested.
- Sunset clauses and periodic review
- Build in a three-year review with clear outcome metrics (impact on creator income, startup formation, model quality, and cultural coverage). Policy without staged review becomes ossified.
- Exemptions for non-commercial research and public-interest uses
- The public good in language models (education, public-health tools, preservation of endangered languages) must not be sacrificed.
Where I worry about implementation
- Who sets the royalty percentage? If it’s too high, it will push companies offshore or kill local startups. If too low, creators will rightly feel cheated.
- How do we ensure the collecting body doesn’t become a monopoly gatekeeper? Competition, audits and judicial review of rates must be available.
- Can this scale globally? India can pioneer a model, but interoperability with other regimes (EU, US, Japan) will determine long-term outcomes for multinational platforms.
My modest, concrete suggestions for policymakers
- Pilot the CRCAT model with defined scope: start with news and published text, learn, then expand to music, images and video.
- Publish the data-disclosure template and invite open feedback from technologists and civil society to make it practical and privacy-preserving.
- Create a small exemptions committee for public-interest projects (education, public health, language preservation).
- Make the rate-setting process public and evidence-driven: require proponents to show how rates affect startup viability and creator incomes.
Closing, personal note
Policy at this intersection — culture, technology and economics — is always messy. I lean toward solutions that preserve creators’ dignity while keeping lanes open for innovators. The blanket licence is an ambitious attempt to square that circle. Its success will depend less on the idea and more on the details: governance, transparency, and a willingness to iterate.
If we do this well, India could set a humane standard: not choosing between creators and AI, but designing a space where both can flourish.
Regards,
Hemen Parekh
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